arXiv AI
Jul 9
Measuring the metacognition of AI
arXiv:2603. 29693v3 Announce Type: replace Abstract: A robust decision-making process must take into account uncertainty, especially when the choice involves inherent risks.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jul 9
Latent graph encoding of multimodal neuroimaging features with generative AI architectures
arXiv:2607. 07027v1 Announce Type: cross Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encoding and latent space processes is crucial for studying structural and functional properties of the brain.
arXiv Machine Learning
Jul 9
Distributed Dynamic Associative Memory via Online Convex Optimization
arXiv:2511. 23347v2 Announce Type: replace Abstract: An associative memory (AM) enables cue-response recall, and it has recently been recognized as a key mechanism underlying modern neural architectures such as Transformers.
arXiv Machine Learning
Jul 9
What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale
arXiv:2510. 13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability.
arXiv AI
Jul 9
SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
arXiv:2607. 07676v1 Announce Type: new Abstract: Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but correct, secure, and maintainable.
arXiv Machine Learning
Jul 9
FourierQK: Spectral Preprocessing of Query-Key Projections Improves Transformer Attention
arXiv:2607. 07478v1 Announce Type: new Abstract: FFT-based spectral preprocessing of learned query-key (Q/K) projections substantially improves transformer attention on character-level language modelling.
arXiv AI
Jul 9
Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows
arXiv:2607. 07504v1 Announce Type: new Abstract: Product data scientists often ask LLM-based agents to help with recurring execution tasks such as cleaning data, writing SQL, choosing statistical tests, and formatting results.
arXiv Machine Learning
Jul 9
Dual Path Attribution: Efficient Attribution for SwiGLU-Transformers through Layer-Wise Target Propagation
arXiv:2603. 19742v2 Announce Type: replace Abstract: Understanding the internal mechanisms of transformer-based large language models (LLMs) is crucial for their reliable deployment and effective operation.
arXiv AI
Jul 9
ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
arXiv:2411. 17077v2 Announce Type: replace-cross Abstract: As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applications use a negated CFG term as a Negative Prompting (NP) to filter out unwanted features from samples.
arXiv AI
Jul 9
Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass
arXiv:2607. 07696v1 Announce Type: cross Abstract: Analytical workloads operating on data stored in external database systems face a fundamental bottleneck: data access is guarded entirely by the database driver, like JDBC or ODBC, forcing all reads through query execution and other driver layers that are not designed for bulk columnar analytics.
arXiv AI
Jul 9
LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models
arXiv:2607. 06918v1 Announce Type: cross Abstract: Pre-trained Vision Foundation Models (VFMs) provide strong visual representations for diverse downstream tasks.
arXiv AI
Jul 9
Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System
arXiv:2607. 06940v1 Announce Type: cross Abstract: The remarkable performance of large language models (LLMs) in linguistic tasks underscores an urgent need for comprehensive evaluation of their response quality.
arXiv AI
Jul 9
Evaluating SageMath-Augmented LLM Agents for Computational and Experimental Mathematics
arXiv:2607. 06820v1 Announce Type: new Abstract: Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS) in agentic LLM workflows underexplored.
arXiv AI
Jul 9
The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents
arXiv:2607. 07436v1 Announce Type: new Abstract: A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures?
arXiv AI
Jul 9
LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting
arXiv:2607. 06623v1 Announce Type: cross Abstract: Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online.
arXiv AI
Jul 9
End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent
arXiv:2607. 06964v1 Announce Type: cross Abstract: Bridging the gap between human pilot intent and autonomous flight operation is critical for real-world electric vertical takeoff and landing (eVTOL) aircraft deployment.
arXiv AI
Jul 9
Informing AI Policy Assessment using Large-Scale Simulation of Interventions
arXiv:2605. 27395v2 Announce Type: replace-cross Abstract: As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has become increasingly challenging for policymakers and researchers.
arXiv AI
Jul 9
Thinking Ahead: Foresight Intelligence in MLLMs and World Model
arXiv:2511. 18735v3 Announce Type: replace-cross Abstract: In this work, we define Foresight Intelligence as the capability to anticipate and interpret future events-an ability essential for applications such as autonomous driving, yet largely overlooked by existing research.
arXiv AI
Jul 9
CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesis
arXiv:2607. 07601v1 Announce Type: cross Abstract: Safety evaluation for autonomous driving is dominated by rare, safety-critical interactions, motivating simulators that can deliberately synthesize corner cases with photorealistic observations.
arXiv Machine Learning
Jul 9
Generalist Vision-Language Models for Fast Radio Burst detection: a zero-shot benchmark against a specialized detector
arXiv:2607. 07382v1 Announce Type: new Abstract: Fast Radio Bursts (FRBs) are millisecond-duration radio transients whose automated detection increasingly relies on highly specialized deep learning models.